diff --git a/static/data/hf_datasets.json b/static/data/hf_datasets.json index f9eac98..84f2e3a 100644 --- a/static/data/hf_datasets.json +++ b/static/data/hf_datasets.json @@ -5768,5 +5768,282 @@ "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/rice_panicle_detection", "examples_image_url": "/img/agml/sample_images/rice_panicle_detection_sample.webp" + }, + { + "name": "date_palm_leaf_disease_classification_iraq", + "machine_learning_task": "image_classification", + "agricultural_task": "disease_classification", + "location": "Iraq", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "date palm" + ], + "sensor_modality": "rgb", + "input_data_format": "image_folder", + "annotation_format": "classLabel", + "num_images": 3000, + "classes": [ + "Bug", + "Dubas", + "Healthy", + "Honey" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1016/j.dib.2023.109371", + "citation": "Mazin, Abdullah; Almayaly, Haider (2023), “Image dataset of infected date palm leaves by dubas insects”, Mendeley Data, V2, doi: 10.17632/2nh364p2bc.2", + "zip_size_bytes": 984229588, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/date_palm_leaf_disease_classification_iraq", + "examples_image_url": "/img/agml/sample_images/date_palm_leaf_disease_classification_iraq_sample.webp" + }, + { + "name": "BananaLSD_leaf_disease_classification", + "machine_learning_task": "image_classification", + "agricultural_task": "disease_classification", + "location": "Bangladesh", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "banana" + ], + "sensor_modality": "rgb", + "input_data_format": "image_folder", + "annotation_format": "classLabel", + "num_images": 937, + "augmented_num_images": 1600, + "augmented_zip_size_bytes": 27508683, + "classes": [ + "cordana", + "healthy", + "pestalotiopsis", + "sigatoka" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1016/j.dib.2023.109608", + "citation": "E Arman, Shifat; Baki Bhuiyan, Md Abdullahil; Abdullah, Hasan Muhammad; Islam, Shariful; Chowdhury, Tahsin Tanha; Hossain, Md. Arban (2023), “Banana Leaf Spot Diseases (BananaLSD) Dataset for Classification of Banana Leaf Diseases Using Machine Learning”, Mendeley Data, V1, doi: 10.17632/9tb7k297ff.1", + "zip_size_bytes": 28172016, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/BananaLSD_leaf_disease_classification", + "examples_image_url": "/img/agml/sample_images/BananaLSD_leaf_disease_classification_sample.webp" + }, + { + "name": "groundnut_leaf_disease_classification_2", + "machine_learning_task": "image_classification", + "agricultural_task": "disease_classification", + "location": "India", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "groundnut" + ], + "sensor_modality": "rgb", + "input_data_format": "image_folder", + "annotation_format": "classLabel", + "num_images": 3058, + "classes": [ + "early_leaf_spot", + "healthy leaf", + "late leaf spot", + "nutrition deficiency", + "rust" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1016/j.dib.2023.109185", + "citation": "Manvikar, Aishwarya; Reddy, Padmanabha (2023), “Dataset of groundnut plant leaf images for classification and detection”, Mendeley Data, V3, doi: 10.17632/22p2vcbxfk.3", + "zip_size_bytes": 809924529, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/groundnut_leaf_disease_classification_2", + "examples_image_url": "/img/agml/sample_images/groundnut_leaf_disease_classification_2_sample.webp" + }, + { + "name": "DIMPSAR_medicinal_plant_classification", + "machine_learning_task": "image_classification", + "agricultural_task": "variety_classification", + "location": "India", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "rgb", + "input_data_format": "image_folder", + "annotation_format": "classLabel", + "num_images": 5945, + "classes": [ + "Aloevera", + "Amla", + "Amruta_Balli", + "Arali", + "Ashoka", + "Ashwagandha", + "Avacado", + "Bamboo", + "Basale", + "Betel", + "Betel_Nut", + "Brahmi", + "Castor", + "Curry_Leaf", + "Doddapatre", + "Ekka", + "Ganike", + "Gauva", + "Geranium", + "Henna", + "Hibiscus", + "Honge", + "Insulin", + "Jasmine", + "Lemon", + "Lemon_grass", + "Mango", + "Mint", + "Nagadali", + "Neem", + "Nithyapushpa", + "Nooni", + "Pappaya", + "Pepper", + "Pomegranate", + "Raktachandini", + "Rose", + "Sapota", + "Tulasi", + "Wood_sorel" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1016/j.dib.2023.109388", + "citation": "B R, Pushpa; Rani, Shobha (2023), “Indian Medicinal Leaves Image Datasets”, Mendeley Data, V3, doi: 10.17632/748f8jkphb.3", + "zip_size_bytes": 264965896, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/DIMPSAR_medicinal_plant_classification", + "examples_image_url": "/img/agml/sample_images/DIMPSAR_medicinal_plant_classification_sample.webp" + }, + { + "name": "DIMPSAR_medicinal_leaf_classification", + "machine_learning_task": "image_classification", + "agricultural_task": "variety_classification", + "location": "India", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "rgb", + "input_data_format": "image_folder", + "annotation_format": "classLabel", + "num_images": 6900, + "classes": [ + "Aloevera", + "Amla", + "Amruthaballi", + "Arali", + "Astma_weed", + "Badipala", + "Balloon_Vine", + "Bamboo", + "Beans", + "Betel", + "Bhrami", + "Bringaraja", + "Caricature", + "Castor", + "Catharanthus", + "Chakte", + "Chilly", + "Citron lime (herelikai)", + "Coffee", + "Common rue(naagdalli)", + "Coriender", + "Curry", + "Doddpathre", + "Drumstick", + "Ekka", + "Eucalyptus", + "Ganigale", + "Ganike", + "Gasagase", + "Ginger", + "Globe Amarnath", + "Guava", + "Henna", + "Hibiscus", + "Honge", + "Insulin", + "Jackfruit", + "Jasmine", + "Kambajala", + "Kasambruga", + "Kohlrabi", + "Lantana", + "Lemon", + "Lemongrass", + "Malabar_Nut", + "Malabar_Spinach", + "Mango", + "Marigold", + "Mint", + "Neem", + "Nelavembu", + "Nerale", + "Nooni", + "Onion", + "Padri", + "Palak(Spinach)", + "Papaya", + "Parijatha", + "Pea", + "Pepper", + "Pomoegranate", + "Pumpkin", + "Raddish", + "Rose", + "Sampige", + "Sapota", + "Seethaashoka", + "Seethapala", + "Spinach1", + "Tamarind", + "Taro", + "Tecoma", + "Thumbe", + "Tomato", + "Tulsi", + "Turmeric", + "ashoka", + "camphor", + "kamakasturi", + "kepala" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1016/j.dib.2023.109388", + "citation": "B R, Pushpa; Rani, Shobha (2023), “Indian Medicinal Leaves Image Datasets”, Mendeley Data, V3, doi: 10.17632/748f8jkphb.3", + "zip_size_bytes": 9484095068, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/DIMPSAR_medicinal_leaf_classification", + "examples_image_url": "/img/agml/sample_images/DIMPSAR_medicinal_leaf_classification_sample.webp" + }, + { + "name": "SoyNet_leaf_health_classification", + "machine_learning_task": "image_classification", + "agricultural_task": "health_classification", + "location": "India", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "soybean" + ], + "sensor_modality": "rgb", + "input_data_format": "image_folder", + "annotation_format": "classLabel", + "num_images": 3655, + "classes": [ + "Disease", + "Healthy" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1016/j.dib.2023.109447", + "citation": "Rajput, Arpan Singh ; Rajput, Alpa Singh; Shukla, Shailja; Thakur, S S (2026), “SoyNet: Indian Soybean Image dataset with quality images captured from the agriculture field ( healthy and disease Images)”, Mendeley Data, V3, doi: 10.17632/w2r855hpx8.3", + "zip_size_bytes": 9963834087, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/SoyNet_leaf_health_classification", + "examples_image_url": "/img/agml/sample_images/SoyNet_leaf_health_classification_sample.webp" } ] diff --git a/static/img/agml/sample_images/BananaLSD_leaf_disease_classification_sample.webp b/static/img/agml/sample_images/BananaLSD_leaf_disease_classification_sample.webp new file mode 100644 index 0000000..4d302b6 Binary files /dev/null and b/static/img/agml/sample_images/BananaLSD_leaf_disease_classification_sample.webp differ diff --git a/static/img/agml/sample_images/DIMPSAR_medicinal_leaf_classification_sample.webp b/static/img/agml/sample_images/DIMPSAR_medicinal_leaf_classification_sample.webp new file mode 100644 index 0000000..98d519a Binary files /dev/null and b/static/img/agml/sample_images/DIMPSAR_medicinal_leaf_classification_sample.webp differ diff --git a/static/img/agml/sample_images/DIMPSAR_medicinal_plant_classification_sample.webp b/static/img/agml/sample_images/DIMPSAR_medicinal_plant_classification_sample.webp new file mode 100644 index 0000000..b77ebee Binary files /dev/null and b/static/img/agml/sample_images/DIMPSAR_medicinal_plant_classification_sample.webp differ diff --git a/static/img/agml/sample_images/SoyNet_leaf_health_classification_sample.webp b/static/img/agml/sample_images/SoyNet_leaf_health_classification_sample.webp new file mode 100644 index 0000000..6feb7e1 Binary files /dev/null and b/static/img/agml/sample_images/SoyNet_leaf_health_classification_sample.webp differ diff --git a/static/img/agml/sample_images/date_palm_leaf_disease_classification_iraq_sample.webp b/static/img/agml/sample_images/date_palm_leaf_disease_classification_iraq_sample.webp new file mode 100644 index 0000000..f697362 Binary files /dev/null and b/static/img/agml/sample_images/date_palm_leaf_disease_classification_iraq_sample.webp differ diff --git a/static/img/agml/sample_images/groundnut_leaf_disease_classification_2_sample.webp b/static/img/agml/sample_images/groundnut_leaf_disease_classification_2_sample.webp new file mode 100644 index 0000000..abf044a Binary files /dev/null and b/static/img/agml/sample_images/groundnut_leaf_disease_classification_2_sample.webp differ